Enhancing traffic capacity of scale-free networks by link-directed strategy

Ma, Jinlong; Han, Weizhan; Guo, Qing; Zhang, Shuai

doi:10.1142/s0129183116500285

Cited by 22 publications

(4 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, not all routing strategies have the same effect on network performance [17]. Prioritizing routes has been necessary for controlling costs, and studies show that high-centrality-first has proved to be an efficient strategy in this regard [18], [19]. Moreover, network efficiency has been identified as an appropriate parameter for describing a network's ability to propagate information and exchange resources [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Robustness and Edge Addition Strategy of Air Transport Networks: A Case Study of “the Belt and Road”

Zhang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Air transportation is of great importance in ''the Belt and Road'' (the B&R) region. The achievement of the B&R initiative relies on the availability, reliability, and safety of air transport infrastructure. A fundamental step is to find the critical elements in network performance. Considering the uneven distributions of population and economy, the current literature focusing on centrality measures in unweighted networks is not sufficient in the B&R region. By differentiating power and centrality in the B&R region, our analysis leads to two conclusions: (1) Deactivating powerful nodes causes a larger decrease in efficiency than deactivating central nodes. This indicates that powerful nodes in the B&R region are more critical than central nodes for network robustness. (2) Strategically adding edges between high powerful and low powerful nodes can enhance the network's ability to exchange resources efficiently. These findings can be used to adjust government policies for air transport configuration to achieve the best network performance and the most cost effective.INDEX TERMS Power, robustness, network performance, air transport, ''the Belt and Road''.

show abstract

Section: Introductionmentioning

confidence: 99%

Robustness and Edge Addition Strategy of Air Transport Networks: A Case Study of “the Belt and Road”

Zhang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…According to the state-of-the-art, the main solutions to these problems lie in improving the traffic capacity of the road [1][2][3]. However, most studies concentrate on in-car advisory systems on lane, speed, and headway [4], strategies design on traffic networks [5], and different model managements [6]. Traditional single-component sensors are difficult to meet the predicted requirements of complex traffic networks.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication

et al. 2021

View full text Add to dashboard Cite

Real-time and reliable short-term traffic state prediction is one of the most critical technologies in intelligent transportation systems (ITS). However, the traffic state is generally perceived by single sensor in existing studies, which is difficult to satisfy the requirement of real-time prediction in complex traffic networks. In this paper, a short-term traffic prediction model based on complex neural network is proposed under the environment of vehicle-to-everything (V2X) communication systems. Firstly, a traffic perception system of multi-source sensors based on V2X communication is proposed and designed. A mobile edge computing (MEC)-assisted architecture is then introduced in a V2X network to facilitate perceptual and computational abilities of the system. Moreover, the graph convolutional network (GCN), the gated recurrent unit (GRU), and the soft-attention mechanism are combined to extract spatiotemporal features of traffic state and integrate them for future prediction. Finally, an intelligent roadside test platform is demonstrated for perception and computation of real-time traffic state. The comparison experiments show that the proposed method can significantly improve the prediction accuracy by comparing with the existing neural network models, which consider one of the spatiotemporal features. In particular, for comparison results of the traffic state prediction and the error value of root mean squared error (RMSE) is reduced by 39.53%, which is the greatest reduction in error occurrences by comparing with the GCN and GRU models in 5, 10, 15 and 30 minutes respectively.

show abstract

“…With the critical role of urban tra c networks in the urban economy, the characteristics and properties of urban tra c network topology have recently gained increasing attention [1][2][3][4][5][6][7][8][9][10][11][12]. Scholars have presented in-depth discussions of the process of tra c planning and design from the perspectives of network topology, tra c ow, tra c congestion, network evolution, cascade failure, and network optimization [13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Optimal Topology of Multilayer Urban Traffic Networks

et al. 2019

View full text Add to dashboard Cite

Previous urban traffic network-based studies have been based mostly on single-layer networks. Based on their shortcomings, starting from the perspective of a multilayer urban traffic network, this paper takes the different anticongestion abilities and network characteristics of various network structures under the condition of traffic congestion as the research object. Then, a comparative experiment is performed via simulation, and the optimal multilayer urban traffic network topology is obtained under different conditions. It is found that these scale-free related multilayer networks have relatively strong ability to support more traffic flows and have higher anticongestion abilities, regardless of whether it is a lower-layer or upper-layer network. The research results are helpful to deepen our understanding of the characteristics of traffic network structures, help scholars further cognize the structural properties of multilayer urban traffic networks, practically help urban traffic network planners to further optimize the urban traffic network, and broaden the study of multilayer traffic networks.

show abstract

Enhancing traffic capacity of scale-free networks by link-directed strategy

Cited by 22 publications

References 60 publications

Robustness and Edge Addition Strategy of Air Transport Networks: A Case Study of “the Belt and Road”

Robustness and Edge Addition Strategy of Air Transport Networks: A Case Study of “the Belt and Road”

Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication

Optimal Topology of Multilayer Urban Traffic Networks

Contact Info

Product

Resources

About